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- W2912787228 abstract "Abstract Nonparametric density estimation methods are designed to estimate the density function f that generates a sample of observations, without assuming a parametric form for f . Rather, f is only assumed to be smooth, where smoothness is defined as the condition that at least a specified number of derivatives are square integrable. Penalized maximum likelihood estimators accomplish this by maximizing a penalized form of the log‐likelihood, where the penalty discourages roughness in the final estimate. The estimator has a Bayesian interpretation, with the penalty function corresponding to the logged prior for the density. The resultant estimator often takes the form of a polynomial spline with knots at the order statistics, and asymptotically is approximately a local‐bandwidth kernel estimator. The method can be adapted to large sparse contingency tables, resulting in cell probability estimators that are consistent under asymptotics that approximate such tables." @default.
- W2912787228 created "2019-02-21" @default.
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- W2912787228 date "2005-02-15" @default.
- W2912787228 modified "2023-10-02" @default.
- W2912787228 title "Penalized Maximum Likelihood" @default.
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- W2912787228 doi "https://doi.org/10.1002/0470011815.b2a15117" @default.
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